发表机构
ByteDance Inc.; Shanghai Jiao Tong University（字节跳动公司; 上海交通大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对MS/MS分子结构解析问题，提出MS-GPT方法，将指纹介导的从头解析重铸为谱诱导后验查询分子语言模型，经校准、排序等操作，在NPLIB1和MassSpecGym上达新最优水平，提升召回率且增加少量推理成本。
AI 中文摘要
从串联质谱（MS/MS）进行分子结构解析是分析化学中的核心逆问题。大多数现有MS/MS识别方法仍依赖参考库或预定义候选集，而从头方法旨在直接从谱生成结构。常见的从头路径是从谱预测分子指纹，然后从中解码结构，可在仅含大分子的语料库上进行解码器预训练。但这种范式存在训练与推理不匹配问题。我们引入MS-GPT，将基于指纹的从头解析重塑为条件分子语言模型的谱诱导后验查询。MS-GPT根据指纹和分子式对分子语言模型进行条件设定，通过活性位密度校准将谱诱导后验转换为接近真实指纹流形的指纹查询带。跨此带采样的候选结构通过生成频率共识进行汇总和排序。轻量级LoRA适配器进一步减轻特定领域的后验偏差，同时保留预训练的分子先验。在NPLIB1和MassSpecGym上，MS-GPT达到了新的最优水平，Top-1/Top-10精确匹配准确率分别为29.8%/41.1%和23.9%/28.7%。候选池缩放表明，高效的自回归分子生成在增加一点推理成本的情况下持续提高召回率。源代码和模型检查点可通过此https URL获取。
英文摘要
Molecular structure elucidation from tandem mass spectra (MS/MS) is a central inverse problem in analytical chemistry. Most existing approaches to MS/MS identification remain tied to reference libraries or predefined candidate sets, whereas de novo methods aim to generate structures directly from spectra. A common de novo route predicts a molecular fingerprint from the spectrum and then decodes structures from it, enabling decoder pretraining on large molecule-only corpora. However, this paradigm creates a training-inference mismatch: the decoder is trained on oracle fingerprints computed from molecules, but at inference it is queried with a noisy spectrum-induced fingerprint posterior that is typically collapsed to a single thresholded fingerprint. We introduce MS-GPT, which recasts fingerprint-mediated de novo elucidation as spectrum-induced posterior querying of a conditional molecule-language model. MS-GPT conditions a molecule-language model on fingerprints and formulas, then converts the spectrum-induced posterior into a band of fingerprint queries near the oracle-fingerprint manifold through active-bit density calibration. Candidates sampled across this band are pooled and ranked by generation-frequency consensus. A lightweight LoRA adapter further mitigates domain-specific posterior bias while preserving the pretrained molecular prior. On NPLIB1 and MassSpecGym, MS-GPT sets a new state of the art, reaching Top-1/Top-10 exact-match accuracy of 29.8\%/41.1\% and 23.9\%/28.7\%, respectively. Candidate-pool scaling shows that efficient autoregressive molecular generation continues to improve recall with a little additional inference cost. The source code and model checkpoints are available at https://github.com/VIKI623/MS-GPT.
Comments18 pages, 14 figures, and 9 tables, including appendices. Source code and model checkpoints are available at https://github.com/VIKI623/MS-GPT
